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Prediction of Hydraulic Cavitation Using 1D Simulation
Technical Paper
2019-28-0129
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
Hydraulic Cavitation is, in many cases, an undesirable occurrence. It is the formation and collapse of air cavities in liquid. In hydraulic devices such as pumps, motors, etc. cavitation causes a great deal of noise, local erosion, damage to components, vibrations, increases oil contamination and a loss of efficiency. There is already established process of predicting cavitation using 3D simulation software. However, the model development is the time-consuming process as well as prediction process is component /subsystem level and cannot be done for various duty cycle operations at architecture level. That requires exploring our research in 1D simulation technique for prediction of cavitation. In this research, we have developed and implemented a methodology/mathematical model for the prediction of hydraulic cavitation in hydraulic system using a 1D simulation technique. For simulation purpose, we have taken an example of simple hydraulic system and predicted the cavitation in one of the component/subsystem of hydraulic system for ambient conditions. The mathematical model proposed based on mass transport equations of vapor, liquid and gas, Rayleigh-Plesset equations, Singhal model and bubble density equations. From simulation results, we conclude that cavitation can be predicted based on bubble dynamics (from estimation of the nuclei of bubble to its collapse), vapor volume fraction, collapse pressure generated at the time of collapse of bubble and maximum impact pressure on the wall of component. The simulation results are validated using 3D simulation software. This method will help to predict cavitation at early stages of design of hydraulic system. Future work consist of the estimation of erosion rate and material damage (life and performance).
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Shinde, P., Rao, A., Jawale, V., and Bandekar, A., "Prediction of Hydraulic Cavitation Using 1D Simulation," SAE Technical Paper 2019-28-0129, 2019, https://doi.org/10.4271/2019-28-0129.Data Sets - Support Documents
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